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Trương Tuấn Anh Tạp chí KHOA HỌC & CÔNG NGHỆ 139(09): 193 - 199

193

TWO-PHASE SHORT-CIRCUIT FAULT DETECTIONS FOR TRANSMISSION

LINE USING WAVELET TRANSFORM AND NEURAL NETWORK

Truong Tuan Anh*

College of Technology - TNU

SUMMARY

Short-circuit is one of the most popular defects on the power transmission lines. Due to the

presence of different types of short-circuit fault, in this paper we’ll consider only the two-phase

short-circuit fault type on a three-phase transmission line. The model use a transmission line at

220kV, 200 km long, frequency at 50Hz with different positions of the failure and different failure

short-circuit resistances to test the proposed solutions. The input signals are only the voltages and

currents at the beginning one-terminal of the transmission line. The math tool selected for this task

is the decomposition algorithms by using Daubechies wavelets and MultiLayer Perceptron neural

network (MLP). The numerical results will show the effectiveness of the proposed method.

Keywords: Fault location, Transmission lines modeling, Reverse problem, short-circuit fault,

Wavelet decomposition

INTRODUCTION*

The problem of short-circuit fault detection

and its parameters estimation is one of the

important tasks in a power transmission

system. An accurate location of the fault

will allow a faster repair and a faster

system restoration. That will also lower the

cost of operation of the system. For each

short-circuit fault, we often need to estimate

three parameters: the moment of the fault, the

position of the fault and the shortage

resistance.

In this paper, we present the idea and the

results of a new method, which will use only

the signals measured at the sending ends of

the lines to detect and locate the two-phase

short circuit happened on the line. This

method will greatly reduce the number of

hardware devices to be used. But we need to

develop more complicate signal processing

algorithms in order to be able to get the

correct results.

The mathematical tool used to process the

data is the signal decomposition by using

Daubechies wavelets. The wavelet solutions

outperform the classical Fourrier

decomposition method because they can give

*

Tel: 0973 143888, Email: [email protected]

not only the information about the harmonic

frequencies in the signals but also the

information about the moment that a specific

frequency starts in a signal [4,5,6,7]. This

advantage fits very well with the fault

detection problems because when a fault occurs,

there will be abrupt changes in signals on the

lines, and as the consequence there will be some

high frequencies newly appear in the signals.

The signals (currents and voltages) of the

three lines will be used to generate the feature

vector for the detection and estimation blocks,

which use the MLP (Multi Layer Perceptron)

- one of the most popular artificial neural

networks - to process the data. The numerical

results will validate the proposed ideas.

WAVELETS AND APPLICATIONS IN

SIGNAL TIME- FREQUENCY ANALYSIS

Wavelet is called an advancer development of

signal decomposition than the classical

Fourier method. In the Fourier method, a

signal is decomposed into sinusoidal

functions as the base functions [6,7]. Because

the basis sinusoidal functions have

“unlimited” domain (i.e. the range in which

we may have function values greater than

small ε is unlimited). Hence when a

frequency appears in the Fourier

decomposition results we can say that the